Learning Y our Way Without Map or Compass: Panoramic T arget Driven Visual Navigation David Watkins-V alls,1, Jingxi Xu,1, Nicholas Waytowich 2 and Peter Allen 1 Abstract -- We present a robot navigation system that uses an imitation learning framework to successfully navigate in complex environments. Our framework takes a pre-built 3D scan of a real environment and trains an agent from pre-generated expert trajectories to navigate to any position given a panoramic view of the goal and the current visual input without relying on map, compass, odometry, GPS or relative position of the target at runtime. Our end-to-end trained agent uses RGB and depth (RGBD) information and can handle large environments (up to 1031 m 2) across multiple rooms (up to 40) and generalizes to unseen targets. We show that when compared to several baselines using deep reinforcement learning and RGBD SLAM, our method (1) requires fewer training examples and less training time, (2) reaches the goal location with higher accuracy, (3) produces better solutions with shorter paths for long-range navigation tasks, and (4) generalizes to unseen environments given an RGBD map of the environment. I NTRODUCTION The ability to navigate efficiently and accurately within an environment is fundamental to intelligent behavior and has been a focus of research in robotics for many years. Traditionally, robotic navigation is solved using model-based methods with an explicit focus on position inference and mapping, such as Simultaneous Localization and Mapping (SLAM) [1]. These models use path planning algorithms, such as Probabilistic Roadmaps (PRM) [2] and Rapidly Exploring Random Trees (RRT) [3], [4] to plan a collision-free path. These methods ignore the rich information from visual input and are highly sensitive to robot odometry and noise in sensor data.

W e review three limitations of BLEU and ROUGE - the most popul ar metrics used to assess reference summaries against hypothesis summ aries, come up with criteria for what a good metric should behave like and propos e concrete ways to use recent Transformers-based Language Models to assess re ference summaries against hypothesis summaries.

Hierarchical neural networks are often used to model inherent structures within dialogues. For goal-oriented dialogues, these models miss a mechanism adhering to the goals and neglect the distinct conversational patterns between two interlocutors. In this work, we propose Goal-Embedded Dual Hierarchical Attentional Encoder-Decoder (G-DuHA) able to center around goals and capture interlocutor-level disparity while modeling goal-oriented dialogues. Experiments on dialogue generation, response generation, and human evaluations demonstrate that the proposed model successfully generates higher-quality, more diverse and goal-centric dialogues. Moreover, we apply data augmentation via goal-oriented dialogue generation for task-oriented dialog systems with better performance achieved.

We explore the interpretation of sound for robot decision-making, inspired by human speech comprehension. While previous methods use natural language processing to translate sound to text, we propose an end-to-end deep neural network which directly learns control polices from images and sound signals. The network is trained using reinforcement learning with auxiliary losses on the sight and sound network branches. We demonstrate our approach on two robots, a TurtleBot3 and a Kuka-IIWA arm, which hear a command word, identify the associated target object, and perform precise control to reach the target. For both systems, we perform ablation studies in simulation to show the effectiveness of our network empirically. We also successfully transfer the policy learned in simulator to a real-world TurtleBot3, which effectively understands word commands, searches for the object, and moves toward that location with more intuitive motion than a traditional motion planner with perfect information.

Analysing Neural Language Models: Contextual Decomposition Reveals Default Reasoning in Number and Gender Assignment Jaap Jumelet jumeletjaap@gmail.com ILLC, University of Amsterdam Abstract Extensive research has recently shown that recurrent neural language models are able to process a wide range of grammatical phenomena. How these models are able to perform these remarkable feats so well, however, is still an open question. To gain more insight into what information LSTMs base their decisions on, we propose a generalisation of Contextual Decomposition (GCD). In particular, this setup enables us to accurately distil which part of a prediction stems from semantic heuristics, which part truly emanates from syntactic cues and which part arise from the model biases themselves instead. We investigate this technique on tasks pertaining to syntactic agreement and coreference resolution and discover that the model strongly relies on a default reasoning effect to perform these tasks. 1 Introduction Modern language models that use deep learning architectures such as LSTMs, bi-LSTMs and Transformers, have shown enormous gains in performance in the last few years and are finding applications in novel domains, ranging from speech recognition and writing assistance to autonomous generation of fake news. Understanding how they reach their predictions has become a key question for NLP, not only for purely scientific, but also for practical and ethical reasons. From a linguistic perspective, a natural approach is to test the extent to which these models have learned classical linguistic constructs, such as inflectional morphology, constituency structure, agreement between verb and subject, filler-gap dependencies, negative polarity or reflexive anaphora. An influential paper using this approach was presented by Linzen et al. (2016), who investigated the performance of an LSTM-based language model on number agreement.

Context-dependent semantic parsing has proven to be an important yet challenging task. To leverage the advances in context-independent semantic parsing, we propose to perform follow-up query analysis, aiming to restate context-dependent natural language queries with contextual information. To accomplish the task, we propose STAR, a novel approach with a well-designed two-phase process. It is parser-independent and able to handle multifarious follow-up scenarios in different domains. Experiments on the FollowUp dataset show that STAR outperforms the state-of-the-art baseline by a large margin of nearly 8%. The superiority on parsing results verifies the feasibility of follow-up query analysis. We also explore the extensibility of STAR on the SQA dataset, which is very promising.

Certain machine behaviors never cease to amaze me. I'm astounded by their ability to learn from their accomplishments and from their interactions with we humans. Unfortunately, many business managers still think of artificial intelligence (AI) and machine learning algorithms as something that will be impossible for them to understand. But I believe that knowing the fundamental principles that underlie the new technologies behind autonomous vehicles, shopping recommendation engines, Alexa and the rest can boost managers' confidence in them and help them make their companies more innovative. The two key drivers of major smart technologies today are machine learning and deep learning.

You might be familiar with the word Artificial intelligence. You might have heard on the radio or might have seen some news stories or a Hollywood movie depicting the same. And why all of the sudden there is such a trend about it in recent times. For starters, the thing is so popular that almost all the top companies from around the world, who are in the FORTUNE 500 are partially or directly involved in evolving or developing or using some form of ARTIFICIAL intelligence. Be it, Tesla, Facebook, Google, Microsoft, OpenAI, and the list goes on and on. Artificial intelligence (AI), generally called machine intelligence, is intelligence determined by machines.

Humans are a species that can adapt to environmental challenges, and over eons this has enabled us to biologically evolve -- an essential characteristic found in animals but absent in AI. Although machine learning has made remarkable progress in complex games such as Go and Dota 2, the skills mastered in these arenas do not necessarily generalize to practical applications in real-world scenarios. The goal for a growing number of researchers is to build a machine intelligence that behaves, learns and evolves more like humans. A new paper from San Francisco-based OpenAI proposes that training models in the children's game of hide-and-seek and pitting them against each other in tens of millions of contests results in the models automatically developing humanlike behaviors that increase their intelligence and improve subsequent performance. Hide-and-seek was selected as a fun starting point mostly due to its simple rules, says the paper's first author, OpenAI Researcher Bowen Baker.

You already know what is Keras and to build a deep learning model using it. Instead of using TensorFlow directly you use Keras to build the model. But wait do you know you can also use the tools that are included in TensorFlow using Keras. There is a tool in the TensorFlow that is Tensorboard that lets you visualize your model's structure and monitor its training. In this entire intuition, you will learn how to view Tensorboard callbacks through Keras and do some analytics to improve your deep learning model.